Shrinkage estimation of the realized relationship matrix

Identification of genetic loci and candidate genes related to soybean flowering through genome wide association study

BACKGROUND

As a photoperiod-sensitive and self-pollinated species, the growth periods traits play important roles in the adaptability and yield of soybean. To examine the genetic architecture of soybean growth periods, we performed a genome-wide association study (GWAS) using a panel of 278 soybean accessions and 34,710 single nucleotide polymorphisms (SNPs) with minor allele frequencies (MAF) higher than 0.04 detected by the specific-locus amplified fragment sequencing (SLAF-seq) with a 6.14-fold average sequencing depth. GWAS was conducted by a compressed mixed linear model (CMLM) involving in both relative kinship and population structure.

RESULTS

GWAS revealed that 37 significant SNP peaks associated with soybean flowering time or other growth periods related traits including full bloom, beginning pod, full pod, beginning seed, and full seed in two or more environments at -log10(P) > 3.75 or -log10(P) > 4.44 were distributed on 14 chromosomes, including chromosome 1, 2, 3, 5, 6, 9, 11, 12, 13, 14, 15, 17, 18, 19. Fourteen SNPs were novel loci and 23 SNPs were located within known QTLs or 75 kb near the known SNPs. Five candidate genes (Glyma.05G101800, Glyma.11G140100, Glyma.11G142900, Glyma.19G099700, Glyma.19G100900) in a 90 kb genomic region of each side of four significant SNPs (Gm5_27111367, Gm11_10629613, Gm11_10950924, Gm19_34768458) based on the average LD decay were homologs of Arabidopsis flowering time genes of AT5G48385.1, AT3G46510.1, AT5G59780.3, AT1G28050.1, and AT3G26790.1. These genes encoding FRI (FRIGIDA), PUB13 (plant U-box 13), MYB59, CONSTANS, and FUS3 proteins respectively might play important roles in controlling soybean growth periods.

CONCLUSIONS

This study identified putative SNP markers associated with soybean growth period traits, which could be used for the marker-assisted selection of soybean growth period traits. Furthermore, the possible candidate genes involved in the control of soybean flowering time were predicted.

Minor alleles are associated with white rust (Albugo occidentalis) susceptibility in spinach (Spinacia oleracea)

Minor alleles (MA) have been associated with disease incidence in human studies, enabling the identification of diagnostic risk factors for various diseases. However, allelic mapping has rarely been performed in plant systems. The goal of this study was to determine whether a difference in MA prevalence is a strong enough risk factor to indicate a likely significant difference in disease resistance against white rust (WR; Albugo occidentalis) in spinach (Spinacia oleracea). We used WR disease severity ratings (WR-DSRs) in a diversity panel of 267 spinach accessions to define resistant- and susceptibility-associated groups within the distribution scores and then tested the single-nucleotide polymorphism (SNP) variants to interrogate the MA prevalence in the most susceptible (MS) vs. most resistant (MR) individuals using permutation-based allelic association tests. A total of 448 minor alleles associated with WR severity were identified in the comparison between the 25% MS and the 25% MR accessions, while the MA were generally similar between the two halves of the interquartile range. The minor alleles in the MS group were distributed across all six chromosomes and made up ~71% of the markers that were also strongly associated with WR in parallel performed genome-wide association study. These results indicate that susceptibility may be highly determined by the disproportionate overrepresentation of minor alleles, which could be used to select for resistant plants. Furthermore, by focusing on the distribution tails, allelic mapping could be used to identify plant markers associated with quantitative traits on the most informative segments of the phenotypic distribution.

Assessing the potential for assisted gene flow using past introduction of Norway spruce in southern Sweden: Local adaptation and genetic basis of quantitative traits in trees

Norway spruce (Picea abies) is a dominant conifer species of major economic importance in northern Europe. Extensive breeding programs were established to improve phenotypic traits of economic interest. In southern Sweden, seeds used to create progeny tests were collected on about 3,000 trees of outstanding phenotype ('plus' trees) across the region. In a companion paper, we showed that some were of local origin but many were recent introductions from the rest of the natural range. The mixed origin of the trees together with partial sequencing of the exome of >1,500 of these trees and phenotypic data retrieved from the Swedish breeding program offered a unique opportunity to dissect the genetic basis of local adaptation of three quantitative traits (height, diameter and bud-burst) and assess the potential of assisted gene flow. Through a combination of multivariate analyses and genome-wide association studies, we showed that there was a very strong effect of geographical origin on growth (height and diameter) and phenology (bud-burst) with trees from southern origins outperforming local provenances. Association studies revealed that growth traits were highly polygenic and bud-burst somewhat less. Hence, our results suggest that assisted gene flow and genomic selection approaches could help to alleviate the effect of climate change on P. abies breeding programs in Sweden.

Genomic basis of European ash tree resistance to ash dieback fungus

Populations of European ash trees (Fraxinus excelsior) are being devastated by the invasive alien fungus Hymenoscyphus fraxineus, which causes ash dieback. We sequenced whole genomic DNA from 1,250 ash trees in 31 DNA pools, each pool containing trees with the same ash dieback damage status in a screening trial and from the same seed-source zone. A genome-wide association study identified 3,149 single nucleotide polymorphisms (SNPs) associated with low versus high ash dieback damage. Sixty-one of the 192 most significant SNPs were in, or close to, genes with putative homologues already known to be involved in pathogen responses in other plant species. We also used the pooled sequence data to train a genomic prediction model, cross-validated using individual whole genome sequence data generated for 75 healthy and 75 damaged trees from a single seed source. The model's genomic estimated breeding values (GEBVs) allocated these 150 trees to their observed health statuses with 67% accuracy using 10,000 SNPs. Using the top 20% of GEBVs from just 200 SNPs, we could predict observed tree health with over 90% accuracy. We infer that ash dieback resistance in F. excelsior is a polygenic trait that should respond well to both natural selection and breeding, which could be accelerated using genomic prediction.

Reference Genome Anchoring of High-Density Markers for Association Mapping and Genomic Prediction in European Winter Wheat

In this study, we anchored genotyping-by-sequencing data to the International Wheat Genome Sequencing Consortium Reference Sequence v1.0 assembly to generate over 40,000 high quality single nucleotide polymorphism markers on a panel of 376 elite European winter wheat varieties released between 1946 and 2007. We compared association mapping and genomic prediction accuracy for a range of productivity traits with previous results based on lower density dominant DArT markers. The results demonstrate that the availability of RefSeq v1.0 supports higher precision trait mapping and provides the density of markers required to obtain accurate predictions of traits controlled by multiple small effect loci, including grain yield.

Developing Growth-Associated Molecular Markers Via High-Throughput Phenotyping in Spinach

High-throughput imaging and genomic information can be combined to optimize marker development. Genome-wide association studies identified loci associated with plant growth traits. We identified candidate genes associated with plant growth and development. Despite advances in sequencing for genotyping, the lack of rapid, accurate, and reproducible phenotyping platforms has hampered efforts to use genetic analysis to predict traits of interest. Therefore, the use of high-throughput systems to phenotype traits related to crop growth, yield, quality, and resistance to biotic and abiotic stresses has become a major asset for breeding. Here, we assessed the efficacy of unmanned aircraft system (UAS)-based high-throughput phenotyping to obtain data for molecular marker development for spinach (Spinacia oleracea L.) improvement. We used a UAS equipped with a red-green-blue sensor to capture raw images of 284 spinach accessions throughout the crop cycle. Processed images generated orthomosaic and digital surface models for estimating canopy cover, canopy volume, and excess greenness index models. In addition, we manually recorded the number of days to bolting. Genome-wide association studies against a single-nucleotide polymorphism (SNP) panel obtained by ddRADseq identified 99 SNPs significantly associated with growth parameters. Some of these SNPs are in transcription factor and stress-response genes with possible roles in plant growth and development. The results underscore the utility of combining aerial imaging and genomic data analysis to optimize marker development. This study lays the foundation for the use of UAS-based high-throughput phenotyping for the molecular breeding of spinach.

A Systematic Gene-Centric Approach to Define Haplotypes and Identify Alleles on the Basis of Dense Single Nucleotide Polymorphism Datasets

A gene-centric approach for haplotype definition was developed and implemented in R. The tool allows for allelic characterization at given loci in germplasm collections. Allelic status at four maturity genes is predicted on the basis of marker genotyping data. Assessing the allelic diversity within a germplasm collection and identifying individuals carrying favorable alleles is challenging. Advances in high-throughput technologies allow the genotyping of many individuals for thousands of markers but bridging the gap between single nucleotide polymorphisms (SNPs) and relevant alleles remains difficult. We developed a systematic approach that defines haplotypes from large SNP catalogs that aims to identify haplotypes that can be equated to alleles at given genes. Unlike haplotype visualization tools, our approach selects SNP markers that flank a gene and define haplotypes that correspond to this gene's alleles. We tested this approach on four known soybean [Glycine max (L.) Merr.] maturity genes (E1, GmGia, GmPhyA3, and GmPhyA2) in a collection of 67 lines and two genotypic datasets [a SNP array and genotyping-by-sequencing (GBS)]. For E1, GmGia, and GmPhyA3, we identified SNP haplotypes such that the allele found at these genes could be accurately predicted from the haplotype in 97.3% of the cases. For these genes, of the 12 known alleles in the collection, 10 and 8 could be correctly predicted from the haplotypes found with the SNP array and GBS datasets, with success rates of 98 and 97% for all allele-line combinations, respectively. The approach proved equally successful for data derived from a SNP array and GBS. However, in the case of GmPhyA2, a lack of markers in the genomic region prevented the identification of alleles, regardless of the dataset. We demonstrate the feasibility and reproducibility of our approach and identify limits to its applicability.

Combining grain yield, protein content and protein quality by multi-trait genomic selection in bread wheat

KEY MESSAGE

Simultaneous genomic selection for grain yield, protein content and dough rheological traits enables the development of resource-use efficient varieties that combine superior yield potential with comparably high end-use quality. Selecting simultaneously for grain yield and baking quality is a major challenge in wheat breeding, and several concepts like grain protein deviations have been developed for shifting the undesirable negative correlation between both traits. The protein quality is, however, not considered in these concepts, although it is an important aspect and might facilitate the selection of genotypes that use available resources more efficiently with respect to the quantity and quality of the final end products. A population of 480 lines from an applied wheat breeding programme that was phenotyped for grain yield, protein content, protein yield and dough rheological traits was thus used to assess the potential of using integrated genomic selection indices to ease selection decisions with regard to the plethora of quality traits. Additionally, the feasibility of achieving a simultaneous genetic improvement in grain yield, protein content and protein quality was investigated to develop more resource-use efficient varieties. Dough rheological traits related to either gluten strength or viscosity were combined in two separate indices, both of which showed a substantially smaller negative trade-off with grain yield than the protein content. Genomic selection indices based on regression deviations for the two latter traits were subsequently extended by the gluten strength or viscosity indices. They revealed a large merit for identifying resource-use efficient genotypes that combine both superior yield potential with comparably high end-use quality. Hence, genomic selection opens up the opportunity for multi-trait selection in early generations, which will most likely increase the efficiency when developing new and improved varieties.

Haplotypes at the Phg-2 Locus Are Determining Pathotype-Specificity of Angular Leaf Spot Resistance in Common Bean

Angular leaf spot (ALS) is one of the most devastating diseases of common bean (Phaseolus vulgaris L.) and causes serious yield losses worldwide. ALS resistance is reportedly pathotype-specific, but little is known about the efficacy of resistance loci against different pathotypes. Here, we report on ALS resistance evaluations of 316 bean lines under greenhouse and field conditions at multiple sites in Colombia and Uganda. Surprisingly, genome-wide association studies revealed only two of the five previously described resistance loci to be significantly associated with ALS resistance. Phg-2 on chromosome eight was crucial for ALS resistance in all trials, while the resistance locus Phg-4 on chromosome 4 was effective against one particular pathotype. Further dissection of Phg-2 uncovered an unprecedented diversity of functional haplotypes for a resistance locus in common bean. DNA sequence-based clustering identified eleven haplotype groups at Phg-2. One haplotype group conferred broad-spectrum ALS resistance, six showed pathotype-specific effects, and the remaining seven did not exhibit clear resistance patterns. Our research highlights the importance of ALS pathotype-specificity for durable resistance management strategies in common bean. Molecular markers co-segregating with resistance loci and haplotypes will increase breeding efficiency for ALS resistance and allow to react faster to future changes in pathogen pressure and composition.

A Frameshift Variant in the CHST9 Gene Identified by Family-Based Whole Genome Sequencing Is Associated with Schizophrenia in Chinese Population

Recent studies imply that rare variants contribute to the risk of schizophrenia, however, the exact variants or genes responsible for this condition are largely unknown. In this study, we conducted whole genome sequencing (WGS) of 20 Chinese families. Each family consisted of at least two affected siblings diagnosed with schizophrenia and at least one unaffected sibling. We examined functional variants that were found in affected sibling(s) but not in unaffected sibling(s) within a family. Matching this criterion, a frameshift heterozygous deletion of CA (–/CA) at chromosome 18:24722722, also referred to as rs752084147, in the Carbohydrate Sulfotransferase 9 (CHST9) gene, was detected in two families. This deletion was confirmed by PCR-based Sanger sequencing. With the observed frequency of 0.00076 in Han Chinese population, we performed both case-control and family-based analyses to evaluate its association with schizophrenia. In the case-control analyses, Chi-square test P-value was 6.80e-12 and the P-value was 0.0008 after one million simulations. In family-based segregation analyses, segregation P-value was 7.72e-7 and simulated P-value was 5.70e-6. For both the case-control and family-based analyses, the CA deletion was significantly associated with schizophrenia in the Chinese population. Further investigation of this gene  is warranted in the development of schizophrenia by utilizing larger and more ethnically diverse samples.

A Novel Gene Underlies Bleomycin-Response Variation in Caenorhabditis elegans

Bleomycin is a powerful chemotherapeutic drug used to treat a variety of cancers. However, individual patients vary in their responses to bleomycin. The identification of genetic differences that underlie this response variation could improve treatment outcomes by tailoring bleomycin dosages to each patient. We used the model organism Caenorhabditis elegans to identify genetic determinants of bleomycin-response differences by performing linkage mapping on recombinants derived from a cross between the laboratory strain (N2) and a wild strain (CB4856). This approach identified a small genomic region on chromosome V that underlies bleomycin-response variation. Using near-isogenic lines, and strains with CRISPR-Cas9 mediated deletions and allele replacements, we discovered that a novel nematode-specific gene (scb-1) is required for bleomycin resistance. Although the mechanism by which this gene causes variation in bleomycin responses is unknown, we suggest that a rare variant present in the CB4856 strain might cause differences in the potential stress-response function of scb-1 between the N2 and CB4856 strains, thereby leading to differences in bleomycin resistance.

Genetic and phenotypic analyses indicate that resistance to flooding stress is uncoupled from performance in cultivated sunflower

Given the rising risk of extreme weather caused by climate change, enhancement of abiotic stress resistance in crops is increasingly urgent. But will the development of stress-resistant cultivars come at the cost of yield under ideal conditions? We hypothesize that this need not be inevitable, because resistance alleles with minimal pleiotropic costs may evade artificial selection and be retained in crop germplasm. Genome-wide association (GWA) analyses for variation in plant performance and flooding response were conducted in cultivated sunflower, a globally important oilseed. We observed broad variation in flooding responses among genotypes. Flooding resistance was not strongly correlated with performance in control conditions, suggesting no inherent trade-offs. Consistent with this finding, we identified a subset of loci conferring flooding resistance, but lacking antagonistic effects on growth. Genetic diversity loss at candidate genes underlying these loci was significantly less than for other resistance genes during cultivated sunflower evolution. Despite bottlenecks associated with domestication and improvement, low-cost resistance alleles remain within the cultivated sunflower gene pool. Thus, development of cultivars that are both flooding-tolerant and highly productive should be straightforward. Results further indicate that estimates of pleiotropic costs from GWA analyses explain, in part, patterns of diversity loss in crop genomes.

Elucidating the genetic basis of biomass accumulation and radiation use efficiency in spring wheat and its role in yield potential

One of the major challenges for plant scientists is increasing wheat (Triticum aestivum) yield potential (YP). A significant bottleneck for increasing YP is achieving increased biomass through optimization of radiation use efficiency (RUE) along the crop cycle. Exotic material such as landraces and synthetic wheat has been incorporated into breeding programmes in an attempt to alleviate this; however, their contribution to YP is still unclear. To understand the genetic basis of biomass accumulation and RUE, we applied genome-wide association study (GWAS) to a panel of 150 elite spring wheat genotypes including many landrace and synthetically derived lines. The panel was evaluated for 31 traits over 2 years under optimal growing conditions and genotyped using the 35K wheat breeders array. Marker-trait association identified 94 SNPs significantly associated with yield, agronomic and phenology-related traits along with RUE and final biomass (BM_PM) at various growth stages that explained 7%-17% of phenotypic variation. Common SNP markers were identified for grain yield, BM_PM and RUE on chromosomes 5A and 7A. Additionally, landrace and synthetic derivative lines showed higher thousand grain weight (TGW), BM_PM and RUE but lower grain number (GM2) and harvest index (HI). Our work demonstrates the use of exotic material as a valuable resource to increase YP. It also provides markers for use in marker-assisted breeding to systematically increase BM_PM, RUE and TGW and avoid the TGW/GM2 and BM_PM/HI trade-off. Thus, achieving greater genetic gains in elite germplasm while also highlighting genomic regions and candidate genes for further study.

High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage

Genomic selection (GS) models have been validated for many quantitative traits in wheat (Triticum aestivum L.) breeding. However, those models are mostly constrained within the same growing cycle and the extension of GS to the case of across cycles has been a challenge, mainly due to the low predictive accuracy resulting from two factors: reduced genetic relationships between different families and augmented environmental variances between cycles. Using the data collected from diverse field conditions at the International Wheat and Maize Improvement Center, we evaluated GS for grain yield in three elite yield trials across three wheat growing cycles. The objective of this project was to employ the secondary traits, canopy temperature, and green normalized difference vegetation index, which are closely associated with grain yield from high-throughput phenotyping platforms, to improve prediction accuracy for grain yield. The ability to predict grain yield was evaluated reciprocally across three cycles with or without secondary traits. Our results indicate that prediction accuracy increased by an average of 146% for grain yield across cycles with secondary traits. In addition, our results suggest that secondary traits phenotyped during wheat heading and early grain filling stages were optimal for enhancing the prediction accuracy for grain yield.

Genomic Prediction of Pumpkin Hybrid Performance

Genomic prediction has become an increasingly popular tool for hybrid performance evaluation in plant breeding mainly because that it can reduce cost and accelerate a breeding program. In this study, we propose a systematic procedure to predict hybrid performance using a genomic selection (GS) model that takes both additive and dominance marker effects into account. We first demonstrate the advantage of the additive-dominance effects model over the only additive effects model through a simulation study. Based on the additive-dominance model, we predict genomic estimated breeding values (GEBVs) for individual hybrid combinations and their parental lines. The GEBV-based specific combining ability (SCA) for each hybrid and general combining ability (GCA) for its parental lines are then derived to quantify the degree of midparent heterosis (MPH) or better-parent heterosis (BPH) of the hybrid. Finally, we estimate the variance components resulting from additive and dominance gene action effects and heritability using a genomic best linear unbiased predictor (g-BLUP) model. These estimates are used to justify the results of the genomic prediction study. A pumpkin ( spp.) data set is given to illustrate the provided procedure. The data set consists of 320 parental lines with 61,179 collected single nucleotide polymorphism (SNP) markers; 119, 120, and 120 phenotypic values of hybrids on three quantitative traits within maxima Duchesne; and 89, 111, and 90 phenotypic values of hybrids on the same three quantitative traits within Dechesne.

Evaluation of the genetic architecture and the potential of genomics-assisted breeding of quality traits in two large panels of durum wheat

New QTL for important quality traits in durum were identified, but for most QTL their effect varies depending on the investigated germplasm. Most of the global durum wheat (Triticum turgidum ssp. durum) production is used for human consumption via pasta and to a lower extent couscous and bulgur. Therefore, durum wheat varieties have to fulfill high demands regarding quality traits. In this study, we evaluated the quality traits protein content, sedimentation volume, falling number, vitreousity and thousand kernel weight in a Central European (CP) and a Southern and Western European panel (SP) with 183 and 159 durum lines, respectively, and investigated their genetic architecture by genome-wide association mapping. Except for protein content, we identified QTL explaining a large proportion of the genotypic variance for different traits. However, most of them were identified only in one panel. Nevertheless, for sedimentation volume a genomic region on chromosome 1B appeared important in both durum panels and a BLAST search against the emmer and bread wheat reference genomes points toward the candidate gene Glu-B3. This was further supported by the protein subunit banding pattern via SDS-PAGE gel electrophoresis. For vitreousity, genomic regions on chromosome 7A explained a larger proportion of the genotypic variance in both panels, whereas one QTL, possibly related to the Pinb-2 locus, also slightly influenced the protein content. Within each panel, high prediction abilities for genomic selection were obtained, which, however, dropped considerably when predicting across both panels. Nevertheless, the across-panel prediction ability was still larger than 0.4 for protein content and sedimentation volume, underlining the potential for genomics-aided durum breeding, if laboratory and logistical facilities are available.

Simultaneous selection for grain yield and protein content in genomics-assisted wheat breeding

Large genetic improvement can be achieved by simultaneous genomic selection for grain yield and protein content when combining different breeding strategies in the form of selection indices. Genomic selection has been implemented in many national and international breeding programmes in recent years. Numerous studies have shown the potential of this new breeding tool; few have, however, taken the simultaneous selection for multiple traits into account that is though common practice in breeding programmes. The simultaneous improvement in grain yield and protein content is thereby a major challenge in wheat breeding due to a severe negative trade-off. Accordingly, the potential and limits of multi-trait selection for this particular trait complex utilizing the vast phenotypic and genomic data collected in an applied wheat breeding programme were investigated in this study. Two breeding strategies based on various genomic-selection indices were compared, which (1) aimed to select high-protein genotypes with acceptable yield potential and (2) develop high-yielding varieties, while maintaining protein content. The prediction accuracy of preliminary yield trials could be strongly improved when combining phenotypic and genomic information in a genomics-assisted selection approach, which surpassed both genomics-based and classical phenotypic selection methods both for single trait predictions and in genomic index selection across years. The employed genomic selection indices mitigated furthermore the negative trade-off between grain yield and protein content leading to a substantial selection response for protein yield, i.e. total seed nitrogen content, which suggested that it is feasible to develop varieties that combine a superior yield potential with comparably high protein content, thus utilizing available nitrogen resources more efficiently.

Use of F2 Bulks in Training Sets for Genomic Prediction of Combining Ability and Hybrid Performance

Developing training sets for genomic prediction in hybrid crops requires producing hybrid seed for a large number of entries. In autogamous crop species (e.g., wheat, rice, rapeseed, cotton) this requires elaborate hybridization systems to prevent self-pollination and presents a significant impediment to the implementation of hybrid breeding in general and genomic selection in particular. An alternative to F1 hybrids are bulks of F2 seed from selfed F1 plants (F1:2). Seed production for F1:2 bulks requires no hybridization system because the number of F1 plants needed for producing enough F1:2 seed for multi-environment testing can be generated by hand-pollination. This study evaluated the suitability of F1:2 bulks for use in training sets for genomic prediction of F1 level general combining ability and hybrid performance, under different degrees of divergence between heterotic groups and modes of gene action, using quantitative genetic theory and simulation of a genomic prediction experiment. The simulation, backed by theory, showed that F1:2 training sets are expected to have a lower prediction accuracy relative to F1 training sets, particularly when heterotic groups have strongly diverged. The accuracy penalty, however, was only modest and mostly because of a lower heritability, rather than because of a difference in F1 and F1:2 genetic values. It is concluded that resorting to F1:2 bulks is, in theory at least, a promising approach to remove the significant complication of a hybridization system from the breeding process.

Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment (G × E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs.

Natural variation in C. elegans arsenic toxicity is explained by differences in branched chain amino acid metabolism

We find that variation in the dbt-1 gene underlies natural differences in Caenorhabditis elegans responses to the toxin arsenic. This gene encodes the E2 subunit of the branched-chain α-keto acid dehydrogenase (BCKDH) complex, a core component of branched-chain amino acid (BCAA) metabolism. We causally linked a non-synonymous variant in the conserved lipoyl domain of DBT-1 to differential arsenic responses. Using targeted metabolomics and chemical supplementation, we demonstrate that differences in responses to arsenic are caused by variation in iso-branched chain fatty acids. Additionally, we show that levels of branched chain fatty acids in human cells are perturbed by arsenic treatment. This finding has broad implications for arsenic toxicity and for arsenic-focused chemotherapeutics across human populations. Our study implicates the BCKDH complex and BCAA metabolism in arsenic responses, demonstrating the power of C. elegans natural genetic diversity to identify novel mechanisms by which environmental toxins affect organismal physiology.

Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Hybrid durum wheat: heterosis of grain yield and quality traits and genetic architecture of anther extrusion

Hybrid durum has a promising yield potential coupled with good quality, but the efficiency of hybrid seed production must be improved. Hybrid breeding is a tremendous success story in many crops, but has not yet made a breakthrough in wheat, mainly due to inefficient hybrid seed production. In this study, we investigated the heterosis for grain yield and important quality traits in durum wheat of 33 hybrids built up from 24 parental lines, as well as the variation in anther extrusion and its genetic architecture in a vast collection of Central European elite durum lines. Average mid-parent heterosis for grain yield was 5.8%, and the best hybrids had a more than one ton per hectare higher grain yield than the best line cultivars. Furthermore, hybrids had a higher grain yield than lines at a given level of protein content or sedimentation value, underpinning their potential for a sustainable agriculture. However, seed set in our experimental hybrid seed production was low. We therefore evaluated 315 elite durum lines for visual anther extrusion, which revealed a large genetic variance and a heritability of 0.66. Results from association mapping suggest a mainly quantitative inheritance of visual anther extrusion with few putative QTL being identified, the largest one explaining less than 20% of the genotypic variance. Genome-wide prediction taking the four largest putative QTL into account yielded a mean cross-validated prediction ability of 0.55. Consequently, breeding for improved male floral characteristics is feasible in durum wheat, but should be mainly based on phenotypic selection.

Exploring and exploiting the genetic variation of Fusarium head blight resistance for genomic-assisted breeding in the elite durum wheat gene pool

KEY MESSAGE

Genomic selection had a higher selection response for FHB resistance than phenotypic selection, while association mapping identified major QTL on chromosome 3B unaffected by plant height and flowering date. Fusarium head blight (FHB) is one of the most destructive diseases of durum wheat. Hence, minimizing losses in yield, quality and avoiding contamination with mycotoxins are of pivotal importance, as durum wheat is mostly used for human consumption. While growing resistant varieties is the most promising approach for controlling this fungal disease, FHB resistance breeding in durum wheat is hampered by the limited variation in the elite gene pool and difficulties in efficiently combining the numerous small-effect resistance quantitative trait loci (QTL) in the same line. We evaluated an international collection of 228 genotyped durum wheat cultivars for FHB resistance over 3 years to investigate the genetic architecture and potential of genomic-assisted breeding for FHB resistance in durum wheat. Plant height was strongly positively correlated with FHB resistance and led to co-localization of plant height and resistance QTL. Nevertheless, a major QTL on chromosome 3B independent of plant height was identified in the same chromosomal interval as reported for the prominent hexaploid resistance QTL Fhb1, though haplotype analysis highlighted the distinctiveness of both QTL. Comparison between phenotypic and genomic selection for FHB resistance revealed a superior prediction ability of the former. However, simulated selection experiments resulted in higher selection responses when using genomic breeding values for early generation selection. An earlier identification of the most promising lines and crossing parents was feasible with a genomic selection index, which suggested a much faster short-term population improvement than previously possible in durum wheat, complementing long-term strategies with exotic resistance donors.

Extensions of BLUP Models for Genomic Prediction in Heterogeneous Populations: Application in a Diverse Switchgrass Sample

Genomic prediction is a useful tool to accelerate genetic gain in selection using DNA marker information. However, this technology typically relies on standard prediction procedures, such as genomic BLUP, that are not designed to accommodate population heterogeneity resulting from differences in marker effects across populations. In this study, we assayed different prediction procedures to capture marker-by-population interactions in genomic prediction models. Prediction procedures included genomic BLUP and two kernel-based extensions of genomic BLUP which explicitly accounted for population heterogeneity. To model population heterogeneity, dissemblance between populations was either depicted by a unique coefficient (as previously reported), or a more flexible function of genetic distance between populations (proposed herein). Models under investigation were applied in a diverse switchgrass sample under two validation schemes: whole-sample calibration, where all individuals except selection candidates are included in the calibration set, and cross-population calibration, where the target population is entirely excluded from the calibration set. First, we showed that using fixed effects, from principal components or putative population groups, appeared detrimental to prediction accuracy, especially in cross-population calibration. Then we showed that modeling population heterogeneity by our proposed procedure resulted in highly significant improvements in model fit. In such cases, gains in accuracy were often positive. These results suggest that population heterogeneity may be parsimoniously captured by kernel methods. However, in cases where improvement in model fit by our proposed procedure is null-to-moderate, ignoring heterogeneity should probably be preferred due to the robustness and simplicity of the standard genomic BLUP model.

Genome-wide haplotype-based association analysis of key traits of plant lodging and architecture of maize identifies major determinants for leaf angle: hapLA4

Traits related to plant lodging and architecture are important determinants of plant productivity in intensive maize cultivation systems. Motivated by the identification of genomic associations with the leaf angle, plant height (PH), ear height (EH) and the EH/PH ratio, we characterized approximately 7,800 haplotypes from a set of high-quality single nucleotide polymorphisms (SNPs), in an association panel consisting of tropical maize inbred lines. The proportion of the phenotypic variations explained by the individual SNPs varied between 7%, for the SNP S1_285330124 (located on chromosome 9 and associated with the EH/PH ratio), and 22%, for the SNP S1_317085830 (located on chromosome 6 and associated with the leaf angle). A total of 40 haplotype blocks were significantly associated with the traits of interest, explaining up to 29% of the phenotypic variation for the leaf angle, corresponding to the haplotype hapLA4.04, which was stable over two growing seasons. Overall, the associations for PH, EH and the EH/PH ratio were environment-specific, which was confirmed by performing a model comparison analysis using the information criteria of Akaike and Schwarz. In addition, five stable haplotypes (83%) and 15 SNPs (75%) were identified for the leaf angle. Finally, approximately 62% of the associated haplotypes (25/40) did not contain SNPs detected in the association study using individual SNP markers. This result confirms the advantage of haplotype-based genome-wide association studies for examining genomic regions that control the determining traits for architecture and lodging in maize plants.

The genetic architecture of teosinte catalyzed and constrained maize domestication

Crop domestication is a well-established system for understanding evolution. We interrogated the genetic architecture of maize domestication from a quantitative genetics perspective. We analyzed domestication-related traits in a maize landrace and a population of its ancestor, teosinte. We observed strong divergence in the underlying genetic architecture including change in the genetic correlations among traits. Despite striking divergence, selection intensities were low for all traits, indicating that selection under domestication can be weaker than natural selection. Analyses suggest total grain weight per plant was not improved and that genetic correlations placed considerable constraint on selection. We hope our results will motivate crop evolutionists to perform similar work in other crops.

The process of evolution under domestication has been studied using phylogenetics, population genetics–genomics, quantitative trait locus (QTL) mapping, gene expression assays, and archaeology. Here, we apply an evolutionary quantitative genetic approach to understand the constraints imposed by the genetic architecture of trait variation in teosinte, the wild ancestor of maize, and the consequences of domestication on genetic architecture. Using modern teosinte and maize landrace populations as proxies for the ancestor and domesticate, respectively, we estimated heritabilities, additive and dominance genetic variances, genetic-by-environment variances, genetic correlations, and genetic covariances for 18 domestication-related traits using realized genomic relationships estimated from genome-wide markers. We found a reduction in heritabilities across most traits, and the reduction is stronger in reproductive traits (size and numbers of grains and ears) than vegetative traits. We observed larger depletion in additive genetic variance than dominance genetic variance. Selection intensities during domestication were weak for all traits, with reproductive traits showing the highest values. For 17 of 18 traits, neutral divergence is rejected, suggesting they were targets of selection during domestication. Yield (total grain weight) per plant is the sole trait that selection does not appear to have improved in maize relative to teosinte. From a multivariate evolution perspective, we identified a strong, nonneutral divergence between teosinte and maize landrace genetic variance–covariance matrices (G-matrices). While the structure of G-matrix in teosinte posed considerable genetic constraint on early domestication, the maize landrace G-matrix indicates that the degree of constraint is more unfavorable for further evolution along the same trajectory.

Genetic Variation and Trait Correlations in an East African Cassava Breeding Population for Genomic Selection

Cassava (Manihot esculenta Crantz) is a major source of dietary carbohydrates for >700 million people globally. However, its long breeding cycle has slowed the rate of genetic gain for target traits. This study aimed to asses genetic variation, the level of inbreeding, and trait correlations in genomic selection breeding cycles. We used phenotypic and genotypic data from the National Crops Resources Research Institute (NaCRRI) foundation population (Cycle 0, C₀) and the progeny (Cycle 1, C₁) derived from crosses of 100 selected C0 clones as progenitors, both to evaluate and optimize genomic selection. The highest broad-sense heritability (H ² = 0.95) and narrow-sense heritability (h ² = 0.81) were recorded for cassava mosaic disease severity and the lowest for root weight per plot (H ² = 0.06 and h ² = 0.00). We observed the highest genetic correlation (r _g= 0.80) between cassava brown streak disease root incidence measured at seedling and clonal stages of evaluation, suggesting the usefulness of seedling data in predicting clonal performance for cassava brown streak root necrosis. Similarly, high genetic correlations were observed between cassava brown streak disease severity (r _g= 0.83) scored at 3 and 6 mo after planting (MAP) and cassava mosaic disease, scored at 3 and 6 MAP (r _g= 0.95), indicating that data obtained on these two diseases at 6 MAP would suffice. Population differentiation between C₀ and C₁ was not well defined, implying that the 100 selected progenitors of C₁ captured the diversity in the C₀. Overall, genetic gain for most traits were observed from C₀ to C₁.

Training set optimization of genomic prediction by means of EthAcc

Genomic prediction is a useful tool for plant and animal breeding programs and is starting to be used to predict human diseases as well. A shortcoming that slows down the genomic selection deployment is that the accuracy of the prediction is not known a priori. We propose EthAcc (Estimated THeoretical ACCuracy) as a method for estimating the accuracy given a training set that is genotyped and phenotyped. EthAcc is based on a causal quantitative trait loci model estimated by a genome-wide association study. This estimated causal model is crucial; therefore, we compared different methods to find the one yielding the best EthAcc. The multilocus mixed model was found to perform the best. We compared EthAcc to accuracy estimators that can be derived via a mixed marker model. We showed that EthAcc is the only approach to correctly estimate the accuracy. Moreover, in case of a structured population, in accordance with the achieved accuracy, EthAcc showed that the biggest training set is not always better than a smaller and closer training set. We then performed training set optimization with EthAcc and compared it to CDmean. EthAcc outperformed CDmean on real datasets from sugar beet, maize, and wheat. Nonetheless, its performance was mainly due to the use of an optimal but inaccessible set as a start of the optimization algorithm. EthAcc's precision and algorithm issues prevent it from reaching a good training set with a random start. Despite this drawback, we demonstrated that a substantial gain in accuracy can be obtained by performing training set optimization.

Association mapping of ectomycorrhizal traits in loblolly pine (Pinus taeda L.)

To understand how diverse mutualisms coevolve and how species adapt to complex environments, a description of the underlying genetic basis of the traits involved must be provided. For example, in diverse coevolving mutualisms, such as the interaction of host plants with a suite of symbiotic mycorrhizal fungi, a key question is whether host plants can coevolve independently with multiple species of symbionts, which depends on whether those interactions are governed independently by separate genes or pleiotropically by shared genes. To provide insight into this question, we employed an association mapping approach in a clonally replicated field experiment of loblolly pine (Pinus taeda L.) to identify genetic components of host traits governing ectomycorrhizal (EM) symbioses (mycorrhizal traits). The relative abundances of different EM fungi as well as the total number of root tips per cm root colonized by EM fungi were analyzed as separate mycorrhizal traits of loblolly pine. Single-nucleotide polymorphisms (SNPs) within candidate genes of loblolly pine were associated with loblolly pine mycorrhizal traits, mapped to the loblolly pine genome, and their putative protein function obtained when available. The results support the hypothesis that ectomycorrhiza formation is governed by host genes of large effect that apparently have independent influences on host interactions with different symbiont species.

Improving and Maintaining Winter Hardiness and Frost Tolerance in Bread Wheat by Genomic Selection

Winter hardiness is a major constraint for autumn sown crops in temperate regions, and thus an important breeding goal in the development of new winter wheat varieties. Winter hardiness is though influenced by many environmental factors rendering phenotypic selection under field conditions a difficult task due to irregular occurrence or absence of winter damage in field trials. Controlled frost tolerance tests in growth chamber experiments are, on the other hand, even with few genotypes, often costly and laborious, which makes a genomic breeding strategy for early generation selection an attractive alternative. The aims of this study were thus to compare the merit of marker-assisted selection using the major frost tolerance QTL Fr-A2 with genomic prediction for winter hardiness and frost tolerance, and to assess the potential of combining both measures with a genomic selection index using a high density marker map or a reduced set of pre-selected markers. Cross-validation within two training populations phenotyped for frost tolerance and winter hardiness underpinned the importance of Fr-A2 for frost tolerance especially when upweighting its effect in genomic prediction models, while a combined genomic selection index increased the prediction accuracy for an independent validation population in comparison to training with winter hardiness data alone. The prediction accuracy could moreover be maintained with pre-selected marker sets, which is highly relevant when employing cost reducing fingerprinting techniques such as targeted genotyping-by-sequencing. Genomic selection showed thus large potential to improve or maintain the performance of winter wheat for these difficult, costly, and laborious to phenotype traits.

Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality

KEY MESSAGE

Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters. Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped for both the training and testing sets was the best model considering phenotyping resources and the gain in predictive ability. The inclusion of correlated traits in the training and testing lines is a strategic approach to replace phenotyping of labor-intensive and high cost traits in a breeding program.

Identification of genomic loci associated with 21chlorophyll fluorescence phenotypes by genome-wide association analysis in soybean

BACKGROUND

Photosynthesis is able to convert solar energy into chemical energy in the form of biomass, but the efficiency of photosynthetic solar energy conversion is low. Chlorophyll fluorescence measurements are rapid, non-destructive, and can provide a wealth of information about the efficiencies of the photosynthetic light reaction processes. Efforts aimed at assessing genetic variation and/or mapping of genetic loci associated with chlorophyll fluorescence phenotypes have been rather limited.

RESULTS

Evaluation of SoySNP50K iSelect SNP Beadchip data from the 189 genotypes phenotyped in this analysis identified 32,453 SNPs with a minor allele frequency (MAF) ≥ 5%. A total of 288 (non-unique) SNPs were significantly associated with one or more of the 21 chlorophyll fluorescence phenotypes. Of these, 155 were unique SNPs and 100 SNPs were only associated with a single fluorescence phenotype, while 28, 11, 2, and 14 SNPs, were associated with two, three, four and five or more fluorescence phenotypes, respectively. The 288 non-unique SNPs represent 155 unique SNPs that mark 53 loci. The 155 unique SNPs included 27 that were associated with three or more phenotypes, and thus were called multi-phenotype SNPs. These 27 multi-phenotype SNPs marked 13 multi-phenotype loci (MPL) identified by individual SNPs associated with multiple chlorophyll fluorescence phenotypes or by more than one SNP located within 0.5 MB of other multi-phenotype SNPs.

CONCLUSION

A search in the genomic regions highlighted by these 13 MPL identified genes with annotations indicating involvement in photosynthetic light dependent reactions. These, as well as loci associated with only one or two chlorophyll fluorescence traits, should be useful to develop a better understanding of the genetic basis of photosynthetic light dependent reactions as a whole as well as of specific components of the electron transport chain in soybean. Accordingly, additional genetic and physiological analyses are necessary to determine the relevance and effectiveness of the identified loci for crop improvement efforts.

Identification of genomic loci associated with 21chlorophyll fluorescence phenotypes by genome-wide association analysis in soybean

BACKGROUND

Photosynthesis is able to convert solar energy into chemical energy in the form of biomass, but the efficiency of photosynthetic solar energy conversion is low. Chlorophyll fluorescence measurements are rapid, non-destructive, and can provide a wealth of information about the efficiencies of the photosynthetic light reaction processes. Efforts aimed at assessing genetic variation and/or mapping of genetic loci associated with chlorophyll fluorescence phenotypes have been rather limited.

RESULTS

Evaluation of SoySNP50K iSelect SNP Beadchip data from the 189 genotypes phenotyped in this analysis identified 32,453 SNPs with a minor allele frequency (MAF) ≥ 5%. A total of 288 (non-unique) SNPs were significantly associated with one or more of the 21 chlorophyll fluorescence phenotypes. Of these, 155 were unique SNPs and 100 SNPs were only associated with a single fluorescence phenotype, while 28, 11, 2, and 14 SNPs, were associated with two, three, four and five or more fluorescence phenotypes, respectively. The 288 non-unique SNPs represent 155 unique SNPs that mark 53 loci. The 155 unique SNPs included 27 that were associated with three or more phenotypes, and thus were called multi-phenotype SNPs. These 27 multi-phenotype SNPs marked 13 multi-phenotype loci (MPL) identified by individual SNPs associated with multiple chlorophyll fluorescence phenotypes or by more than one SNP located within 0.5 MB of other multi-phenotype SNPs.

CONCLUSION

A search in the genomic regions highlighted by these 13 MPL identified genes with annotations indicating involvement in photosynthetic light dependent reactions. These, as well as loci associated with only one or two chlorophyll fluorescence traits, should be useful to develop a better understanding of the genetic basis of photosynthetic light dependent reactions as a whole as well as of specific components of the electron transport chain in soybean. Accordingly, additional genetic and physiological analyses are necessary to determine the relevance and effectiveness of the identified loci for crop improvement efforts.

Assessing the variation and genetic architecture of asparagine content in wheat: What can plant breeding contribute to a reduction in the acrylamide precursor?

A large genetic variation, moderately high heritability, and promising prediction ability for genomic selection show that wheat breeding can substantially reduce the acrylamide forming potential in bread wheat by a reduction in its precursor asparagine. Acrylamide is a potentially carcinogenic substance that is formed in baked products of wheat via the Maillard reaction from carbonyl sources and asparagine. In bread, the acrylamide content increases almost linearly with the asparagine content of the wheat grains. Our objective was, therefore, to investigate the potential of wheat breeding to contribute to a reduction in acrylamide by decreasing the asparagine content in wheat grains. To this end, we evaluated 149 wheat varieties from Central Europe at three locations for asparagine content, as well as for sulfur content, and five important quality traits regularly assessed in bread wheat breeding. The mean asparagine content ranged from 143.25 to 392.75 mg/kg for the different wheat varieties, thus underlining the possibility to reduce the acrylamide content of baked wheat products considerably by selecting appropriate varieties. Furthermore, a moderately high heritability of 0.65 and no negative correlations with quality traits like protein content, sedimentation volume and falling number show that breeding of quality wheat with low asparagine content is feasible. Genome-wide association mapping identified few QTL for asparagine content, the largest explaining 18% of the genotypic variance. Combining these QTL with a genome-wide prediction approach yielded a mean cross-validated prediction ability of 0.62. As we observed a high genotype-by-environment interaction for asparagine content, we recommend the costly and slow laboratory analysis only for late breeding generations, while selection in early generations could be based on marker-assisted or genomic selection.

Prospects and Challenges of Applied Genomic Selection-A New Paradigm in Breeding for Grain Yield in Bread Wheat

Genomic selection (GS) has been promising for increasing genetic gains in several species. Therefore, we evaluated the potential integration of GS for grain yield (GY) in bread wheat ( L.) in CIMMYT's elite yield trial nurseries. We observed that the genomic prediction accuracies within nurseries (0.44 and 0.35) were substantially higher than across-nursery accuracies (0.15 and 0.05) for GY evaluated in the bed and flat planting systems, respectively. The accuracies from using only a subset of 251 genotyping-by-sequencing markers were comparable to the accuracies using all 2038 markers. We also used the item-based collaborative filtering approach for incorporating other related traits in predicting GY and observed that it outperformed genomic predictions across nurseries, but was less predictive when trait correlations with GY were low. Furthermore, we compared GS and phenotypic selections (PS) and observed that at a selection intensity of 0.5, GS could select a maximum of 70.9 and 61.5% of the top lines and discard 71.5 and 60.5% of the poor lines selected or discarded by PS within and across nurseries, respectively. Comparisons of GS and pedigree-based predictions revealed that the advantage of GS over the pedigree was moderate in populations without full-sibs. However, GS was less advantageous for within-family selections in elite families with few full-sibs and minimal Mendelian sampling variance. Overall, our results demonstrate the importance of applying GS for GY at the appropriate stage of the breeding cycle, and we speculate that gains can be maximized if it is implemented in early-generation within-family selections.

Integrating GWAS and gene expression data for functional characterization of resistance to white mould in soya bean

White mould of soya bean, caused by Sclerotinia sclerotiorum (Lib.) de Bary, is a necrotrophic fungus capable of infecting a wide range of plants. To dissect the genetic architecture of resistance to white mould, a high-density customized single nucleotide polymorphism (SNP) array (52 041 SNPs) was used to genotype two soya bean diversity panels. Combined with resistance variation data observed in the field and greenhouse environments, genome-wide association studies (GWASs) were conducted to identify quantitative trait loci (QTL) controlling resistance against white mould. Results showed that 16 and 11 loci were found significantly associated with resistance in field and greenhouse, respectively. Of these, eight loci localized to previously mapped QTL intervals and one locus had significant associations with resistance across both environments. The expression level changes in genes located in GWAS-identified loci were assessed between partially resistant and susceptible genotypes through a RNA-seq analysis of the stem tissue collected at various time points after inoculation. A set of genes with diverse biological functionalities were identified as strong candidates underlying white mould resistance. Moreover, we found that genomic prediction models outperformed predictions based on significant SNPs. Prediction accuracies ranged from 0.48 to 0.64 for disease index measured in field experiments. The integrative methods, including GWAS, RNA-seq and genomic selection (GS), applied in this study facilitated the identification of causal variants, enhanced our understanding of mechanisms of white mould resistance and provided valuable information regarding breeding for disease resistance through genomic selection in soya bean.

Multivariate GBLUP Improves Accuracy of Genomic Selection for Yield and Fruit Weight in Biparental Populations of Vaccinium macrocarpon Ait

The development of high-throughput genotyping has made genome-wide association (GWAS) and genomic selection (GS) applications possible for both model and non-model species. The exploitation of genome-assisted approaches could greatly benefit breeding efforts in American cranberry (Vaccinium macrocarpon) and other minor crops. Using biparental populations with different degrees of relatedness, we evaluated multiple GS methods for total yield (TY) and mean fruit weight (MFW). Specifically, we compared predictive ability (PA) differences between univariate and multivariate genomic best linear unbiased predictors (GBLUP and MGBLUP, respectively). We found that MGBLUP provided higher predictive ability (PA) than GBLUP, in scenarios with medium genetic correlation (8-17% increase with corg~0.6) and high genetic correlations (25-156% with corg~0.9), but found no increase when genetic correlation was low. In addition, we found that only a few hundred single nucleotide polymorphism (SNP) markers are needed to reach a plateau in PA for both traits in the biparental populations studied (in full linkage disequilibrium). We observed that higher resemblance among individuals in the training (TP) and validation (VP) populations provided greater PA. Although multivariate GS methods are available, genetic correlations and other factors need to be carefully considered when applying these methods for genetic improvement.

Genome-Wide Association Mapping of Seedling Heat Tolerance in Winter Wheat

Heat stress during the seedling stage of early-planted winter wheat (Triticum aestivum L.) is one of the most abiotic stresses of the crop restricting forage and grain production in the Southern Plains of the United States. To map quantitative trait loci (QTLs) and identify single-nucleotide polymorphism (SNP) markers associated with seedling heat tolerance, a genome-wide association mapping study (GWAS) was conducted using 200 diverse representative lines of the hard red winter wheat association mapping panel, which was established by the Triticeae Coordinated Agricultural Project (TCAP) and genotyped with the wheat iSelect 90K SNP array. The plants were initially planted under optimal temperature conditions in two growth chambers. At the three-leaf stage, one chamber was set to 40/35°C day/night as heat stress treatment, while the other chamber was kept at optimal temperature (25/20°C day/night) as control for 14 days. Data were collected on leaf chlorophyll content, shoot length, number of leaves per seedling, and seedling recovery after removal of heat stress treatment. Phenotypic variability for seedling heat tolerance among wheat lines was observed in this study. Using the mixed linear model (MLM), we detected multiple significant QTLs for seedling heat tolerance on different chromosomes. Some of the QTLs were detected on chromosomes that were previously reported to harbor QTLs for heat tolerance during the flowering stage of wheat. These results suggest that some heat tolerance QTLs are effective from the seedling to reproductive stages in wheat. However, new QTLs that have never been reported at the reproductive stage were found responding to seedling heat stress in the present study. Candidate gene analysis revealed high sequence similarities of some significant loci with candidate genes involved in plant stress responses including heat, drought, and salt stress. This study provides valuable information about the genetic basis of seedling heat tolerance in wheat. To the best of our knowledge, this is the first GWAS to map QTLs associated with seedling heat tolerance targeting early planting of dual-purpose winter wheat. The SNP markers identified in this study will be used for marker-assisted selection (MAS) of seedling heat tolerance during dual-purpose wheat breeding.

Unlocking the novel genetic diversity and population structure of synthetic Hexaploid wheat

Background

Synthetic hexaploid wheat (SHW) is a reconstitution of hexaploid wheat from its progenitors (Triticum turgidum ssp. durum L.; AABB x Aegilops tauschii Coss.; DD) and has novel sources of genetic diversity for broadening the genetic base of elite bread wheat (BW) germplasm (T. aestivum L). Understanding the diversity and population structure of SHWs will facilitate their use in wheat breeding programs. Our objectives were to understand the genetic diversity and population structure of SHWs and compare the genetic diversity of SHWs with elite BW cultivars and demonstrate the potential of SHWs to broaden the genetic base of modern wheat germplasm.

Results

The genotyping-by-sequencing of SHW provided 35,939 high-quality single nucleotide polymorphisms (SNPs) that were distributed across the A (33%), B (36%), and D (31%) genomes. The percentage of SNPs on the D genome was nearly same as the other two genomes, unlike in BW cultivars where the D genome polymorphism is generally much lower than the A and B genomes. This indicates the presence of high variation in the D genome in the SHWs. The D genome gene diversity of SHWs was 88.2% higher than that found in a sample of elite BW cultivars. Population structure analysis revealed that SHWs could be separated into two subgroups, mainly differentiated by geographical location of durum parents and growth habit of the crop (spring and winter type). Further population structure analysis of durum and Ae. parents separately identified two subgroups, mainly based on type of parents used. Although Ae. tauschii parents were divided into two sub-species: Ae. tauschii ssp. tauschii and ssp. strangulate, they were not clearly distinguished in the diversity analysis outcome. Population differentiation between SHWs (Spring_SHW and Winter_SHW) samples using analysis of molecular variance indicated 17.43% of genetic variance between populations and the remainder within populations.

Conclusions

SHWs were diverse and had a clearly distinguished population structure identified through GBS-derived SNPs. The results of this study will provide valuable information for wheat genetic improvement through inclusion of novel genetic variation and is a prerequisite for association mapping and genomic selection to unravel economically important marker-trait associations and for cultivar development.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4969-2) contains supplementary material, which is available to authorized users.

Haplotype-based genotyping-by-sequencing in oat genome research

In a de novo genotyping-by-sequencing (GBS) analysis of short, 64-base tag-level haplotypes in 4657 accessions of cultivated oat, we discovered 164741 tag-level (TL) genetic variants containing 241224 SNPs. From this, the marker density of an oat consensus map was increased by the addition of more than 70000 loci. The mapped TL genotypes of a 635-line diversity panel were used to infer chromosome-level (CL) haplotype maps. These maps revealed differences in the number and size of haplotype blocks, as well as differences in haplotype diversity between chromosomes and subsets of the diversity panel. We then explored potential benefits of SNP vs. TL vs. CL GBS variants for mapping, high-resolution genome analysis and genomic selection in oats. A combined genome-wide association study (GWAS) of heading date from multiple locations using both TL haplotypes and individual SNP markers identified 184 significant associations. A comparative GWAS using TL haplotypes, CL haplotype blocks and their combinations demonstrated the superiority of using TL haplotype markers. Using a principal component-based genome-wide scan, genomic regions containing signatures of selection were identified. These regions may contain genes that are responsible for the local adaptation of oats to Northern American conditions. Genomic selection for heading date using TL haplotypes or SNP markers gave comparable and promising prediction accuracies of up to r = 0.74. Genomic selection carried out in an independent calibration and test population for heading date gave promising prediction accuracies that ranged between r = 0.42 and 0.67. In conclusion, TL haplotype GBS-derived markers facilitate genome analysis and genomic selection in oat.

Identification of Genomic Regions Contributing to Protein Accumulation in Wheat under Well-Watered and Water Deficit Growth Conditions

Sustaining wheat production under low-input conditions through development and identifying genotypes with enhanced nutritional quality are two current concerns of wheat breeders. Wheat grain total protein content, to no small extent, determines the economic and nutritive value of wheat. Therefore, the objectives of this study are to identify accessions with high and low grain protein content (GPC) under well-watered and water-deficit growth conditions and to locate genomic regions that contribute to GPC accumulation. Spring wheat grains obtained from 2111 accessions that were grown under well-watered and water-deficit conditions were assessed for GPC using near-infrared spectroscopy (NIR). Results indicated significant influences of moisture, genotype, and genotype × environment interaction on the GPC accumulation. Furthermore, genotypes exhibited a wide range of variation for GPC, indicating the presence of high levels of genetic variability among the studied accessions. Around 366 (166 with high GPC and 200 with low GPC) wheat genotypes performed relatively the same across environments, which implies that GPC accumulation in these genotypes was less responsive to water deficit. Genome-wide association mapping results indicated that seven single nucleotide polymorphism (SNPs) were linked with GPC under well-watered growth conditions, while another six SNPs were linked with GPC under water-deficit conditions only. Moreover, 10 SNPs were linked with GPC under both well-watered and water-deficit conditions. These results emphasize the importance of using diverse, worldwide germplasm to dissect the genetic architecture of GPC in wheat and identify accessions that might be potential parents for high GPC in wheat breeding programs.

Genomic prediction accuracy for switchgrass traits related to bioenergy within differentiated populations

BACKGROUND

Switchgrass breeders need to improve the rates of genetic gain in many bioenergy-related traits in order to create improved cultivars that are higher yielding and have optimal biomass composition. One way to achieve this is through genomic selection. However, the heritability of traits needs to be determined as well as the accuracy of prediction in order to determine if efficient selection is possible.

RESULTS

Using five distinct switchgrass populations comprised of three lowland, one upland and one hybrid accession, the accuracy of genomic predictions under different cross-validation strategies and prediction methods was investigated. Individual genotypes were collected using GBS while kin-BLUP, partial least squares, sparse partial least squares, and BayesB methods were employed to predict yield, morphological, and NIRS-based compositional data collected in 2012-2013 from a replicated Nebraska field trial. Population structure was assessed by F statistics which ranged from 0.3952 between lowland and upland accessions to 0.0131 among the lowland accessions. Prediction accuracy ranged from 0.57-0.52 for cell wall soluble glucose and fructose respectively, to insignificant for traits with low repeatability. Ratios of heritability across to within-population ranged from 15 to 0.6.

CONCLUSIONS

Accuracy was significantly affected by both cross-validation strategy and trait. Accounting for population structure with a cross-validation strategy constrained by accession resulted in accuracies that were 69% lower than apparent accuracies using unconstrained cross-validation. Less accurate genomic selection is anticipated when most of the phenotypic variation exists between populations such as with spring regreening and yield phenotypes.

Genome-wide association mapping for resistance to leaf rust, stripe rust and tan spot in wheat reveals potential candidate genes

KEY MESSAGE

Genome-wide association mapping in conjunction with population sequencing map and Ensembl plants was used to identify markers/candidate genes linked to leaf rust, stripe rust and tan spot resistance in wheat. Leaf rust (LR), stripe rust (YR) and tan spot (TS) are some of the important foliar diseases in wheat (Triticum aestivum L.). To identify candidate resistance genes for these diseases in CIMMYT's (International Maize and Wheat Improvement Center) International bread wheat screening nurseries, we used genome-wide association studies (GWAS) in conjunction with information from the population sequencing map and Ensembl plants. Wheat entries were genotyped using genotyping-by-sequencing and phenotyped in replicated trials. Using a mixed linear model, we observed that seedling resistance to LR was associated with 12 markers on chromosomes 1DS, 2AS, 2BL, 3B, 4AL, 6AS and 6AL, and seedling resistance to TS was associated with 14 markers on chromosomes 1AS, 2AL, 2BL, 3AS, 3AL, 3B, 6AS and 6AL. Seedling and adult plant resistance (APR) to YR were associated with several markers at the distal end of chromosome 2AS. In addition, YR APR was also associated with markers on chromosomes 2DL, 3B and 7DS. The potential candidate genes for these diseases included several resistance genes, receptor-like serine/threonine-protein kinases and defense-related enzymes. However, extensive LD in wheat that decays at about 5 × 107 bps, poses a huge challenge for delineating candidate gene intervals and candidates should be further mapped, functionally characterized and validated. We also explored a segment on chromosome 2AS associated with multiple disease resistance and identified seventeen disease resistance linked genes. We conclude that identifying candidate genes linked to significant markers in GWAS is feasible in wheat, thus creating opportunities for accelerating molecular breeding.

Identification of Novel Genomic Loci Associated with Soybean Shoot Tissue Macro- and Micronutrient Concentrations

The mineral composition of crop shoot tissues is important for yield formation and nutrient remobilization to seeds. The natural diversity that exists within crop species can be used to investigate mechanisms that define plant mineral composition and to identify important genomic loci for these processes. The objective of this study was to determine shoot mineral nutrient concentrations in genetically diverse soybean [ (L.) Merr.] genotypes and to identify genomic regions associated with concentrations of different nutrients in shoot tissue. The genotypes were grown at two locations in 2 yr and characterized for macronutrient (Ca, Mg, P, K, and S) and micronutrient (B, Cu, Fe, Mn, and Zn) concentrations in shoot tissues. Genome-wide association studies were conducted with 31,748 single nucleotide polymorphisms (SNPs) via a unified mixed model to identify SNPs associated with macro- and micronutrient concentrations. The number of putative loci identified for the macronutrients ranged from 11 for Ca to 20 for K. For the micronutrients, the number ranged from 10 for Mn to 24 for Fe. In addition to colocated loci for multiple nutrients, 22 individual SNPs were associated with more than one nutrient such that 11 different nutrient combinations were encompassed by these SNPs. Ultimately, the putative loci identified in this study will need to be confirmed and are expected to aid in the identification of new sources of variation for use in soybean breeding programs as well as for mechanistic studies aimed at understanding the regulation of mineral nutrient uptake, translocation, and shoot tissue concentrations.

Simultaneous improvement of grain yield and protein content in durum wheat by different phenotypic indices and genomic selection

Simultaneous improvement of protein content and grain yield by index selection is possible but its efficiency largely depends on the weighting of the single traits. The genetic architecture of these indices is similar to that of the primary traits. Grain yield and protein content are of major importance in durum wheat breeding, but their negative correlation has hampered their simultaneous improvement. To account for this in wheat breeding, the grain protein deviation (GPD) and the protein yield were proposed as targets for selection. The aim of this work was to investigate the potential of different indices to simultaneously improve grain yield and protein content in durum wheat and to evaluate their genetic architecture towards genomics-assisted breeding. To this end, we investigated two different durum wheat panels comprising 159 and 189 genotypes, which were tested in multiple field locations across Europe and genotyped by a genotyping-by-sequencing approach. The phenotypic analyses revealed significant genetic variances for all traits and heritabilities of the phenotypic indices that were in a similar range as those of grain yield and protein content. The GPD showed a high and positive correlation with protein content, whereas protein yield was highly and positively correlated with grain yield. Thus, selecting for a high GPD would mainly increase the protein content whereas a selection based on protein yield would mainly improve grain yield, but a combination of both indices allows to balance this selection. The genome-wide association mapping revealed a complex genetic architecture for all traits with most QTL having small effects and being detected only in one germplasm set, thus limiting the potential of marker-assisted selection for trait improvement. By contrast, genome-wide prediction appeared promising but its performance strongly depends on the relatedness between training and prediction sets.

Diverse Chromosomal Locations of Quantitative Trait Loci for Tolerance to Maize chlorotic mottle virus in Five Maize Populations

The recent rapid emergence of maize lethal necrosis (MLN), caused by coinfection of maize with Maize chlorotic mottle virus (MCMV) and a second virus usually from the family Potyviridae, is causing extensive losses for farmers in East Africa, Southeast Asia, and South America. Although the genetic basis of resistance to potyviruses is well understood in maize, little was known about resistance to MCMV. The responses of five maize inbred lines (KS23-5, KS23-6, N211, DR, and Oh1VI) to inoculation with MCMV, Sugarcane mosaic virus, and MLN were characterized. All five lines developed fewer symptoms than susceptible controls after inoculation with MCMV; however, the virus was detected in systemic leaf tissue from each of the lines similarly to susceptible controls, indicating that the lines were tolerant of MCMV rather than resistant to it. Except for KS23-5, the inbred lines also developed fewer symptoms after inoculation with MLN than susceptible controls. To identify genetic loci associated with MCMV tolerance, large F₂ or recombinant inbred populations were evaluated for their phenotypic responses to MCMV, and the most resistant and susceptible plants were genotyped by sequencing. One to four quantitative trait loci (QTL) were identified in each tolerant population using recombination frequency and positional mapping strategies. In contrast to previous studies of virus resistance in maize, the chromosomal positions and genetic character of the QTL were unique to each population. The results suggest that different, genotype-specific mechanisms are associated with MCMV tolerance in maize. These results will allow for the development of markers for marker-assisted selection of MCMV- and MLN-tolerant maize hybrids for disease control.

Accounting for Genotype-by-Environment Interactions and Residual Genetic Variation in Genomic Selection for Water-Soluble Carbohydrate Concentration in Wheat

Abiotic stress tolerance traits are often complex and recalcitrant targets for conventional breeding improvement in many crop species. This study evaluated the potential of genomic selection to predict water-soluble carbohydrate concentration (WSCC), an important drought tolerance trait, in wheat under field conditions. A panel of 358 varieties and breeding lines constrained for maturity was evaluated under rainfed and irrigated treatments across two locations and two years. Whole-genome marker profiles and factor analytic mixed models were used to generate genomic estimated breeding values (GEBVs) for specific environments and environment groups. Additive genetic variance was smaller than residual genetic variance for WSCC, such that genotypic values were dominated by residual genetic effects rather than additive breeding values. As a result, GEBVs were not accurate predictors of genotypic values of the extant lines, but GEBVs should be reliable selection criteria to choose parents for intermating to produce new populations. The accuracy of GEBVs for untested lines was sufficient to increase predicted genetic gain from genomic selection per unit time compared to phenotypic selection if the breeding cycle is reduced by half by the use of GEBVs in off-season generations. Further, genomic prediction accuracy depended on having phenotypic data from environments with strong correlations with target production environments to build prediction models. By combining high-density marker genotypes, stress-managed field evaluations, and mixed models that model simultaneously covariances among genotypes and covariances of complex trait performance between pairs of environments, we were able to train models with good accuracy to facilitate genetic gain from genomic selection.

Parallel altitudinal clines reveal trends in adaptive evolution of genome size in Zea mays

While the vast majority of genome size variation in plants is due to differences in repetitive sequence, we know little about how selection acts on repeat content in natural populations. Here we investigate parallel changes in intraspecific genome size and repeat content of domesticated maize (Zea mays) landraces and their wild relative teosinte across altitudinal gradients in Mesoamerica and South America. We combine genotyping, low coverage whole-genome sequence data, and flow cytometry to test for evidence of selection on genome size and individual repeat abundance. We find that population structure alone cannot explain the observed variation, implying that clinal patterns of genome size are maintained by natural selection. Our modeling additionally provides evidence of selection on individual heterochromatic knob repeats, likely due to their large individual contribution to genome size. To better understand the phenotypes driving selection on genome size, we conducted a growth chamber experiment using a population of highland teosinte exhibiting extensive variation in genome size. We find weak support for a positive correlation between genome size and cell size, but stronger support for a negative correlation between genome size and the rate of cell production. Reanalyzing published data of cell counts in maize shoot apical meristems, we then identify a negative correlation between cell production rate and flowering time. Together, our data suggest a model in which variation in genome size is driven by natural selection on flowering time across altitudinal clines, connecting intraspecific variation in repetitive sequence to important differences in adaptive phenotypes.

Genome size in plants can vary by orders of magnitude, but this variation has long been considered to be of little functional consequence. Studying three independent adaptations to high altitude in Zea mays, we find that genome size experiences parallel pressures from natural selection, causing a reduction in genome size with increasing altitude. Though reductions in overall repetitive content are responsible for the genome size change, we find that only those individual loci contributing most to the variation in genome size are individually targeted by selection. To identify the phenotype influenced by genome size, we study how variation in genome size within a single wild population impacts leaf growth and cell division. We find that genome size variation correlates negatively with the rate of cell division, suggesting that individuals with larger genomes require longer to complete a mitotic cycle. Finally, we reanalyze data from maize inbreds to show that faster cell division is correlated with earlier flowering, connecting observed variation in genome size to an important adaptive phenotype.

Parallel altitudinal clines reveal trends in adaptive evolution of genome size in Zea mays

While the vast majority of genome size variation in plants is due to differences in repetitive sequence, we know little about how selection acts on repeat content in natural populations. Here we investigate parallel changes in intraspecific genome size and repeat content of domesticated maize (Zea mays) landraces and their wild relative teosinte across altitudinal gradients in Mesoamerica and South America. We combine genotyping, low coverage whole-genome sequence data, and flow cytometry to test for evidence of selection on genome size and individual repeat abundance. We find that population structure alone cannot explain the observed variation, implying that clinal patterns of genome size are maintained by natural selection. Our modeling additionally provides evidence of selection on individual heterochromatic knob repeats, likely due to their large individual contribution to genome size. To better understand the phenotypes driving selection on genome size, we conducted a growth chamber experiment using a population of highland teosinte exhibiting extensive variation in genome size. We find weak support for a positive correlation between genome size and cell size, but stronger support for a negative correlation between genome size and the rate of cell production. Reanalyzing published data of cell counts in maize shoot apical meristems, we then identify a negative correlation between cell production rate and flowering time. Together, our data suggest a model in which variation in genome size is driven by natural selection on flowering time across altitudinal clines, connecting intraspecific variation in repetitive sequence to important differences in adaptive phenotypes.

Advantages and limitations of multiple-trait genomic prediction for Fusarium head blight severity in hybrid wheat (Triticum aestivum L.)

Predictabilities for wheat hybrids less related to the estimation set were improved by shifting from single- to multiple-trait genomic prediction of Fusarium head blight severity. Breeding for improved Fusarium head blight resistance (FHBr) of wheat is a very laborious and expensive task. FHBr complexity is mainly due to its highly polygenic nature and because FHB severity (FHBs) is greatly influenced by the environment. Associated traits plant height and heading date may provide additional information related to FHBr, but this is ignored in single-trait genomic prediction (STGP). The aim of our study was to explore the benefits in predictabilities of multiple-trait genomic prediction (MTGP) over STGP of target trait FHBs in a population of 1604 wheat hybrids using information on 17,372 single nucleotide polymorphism markers along with indicator traits plant height and heading date. The additive inheritance of FHBs allowed accurate hybrid performance predictions using information on general combining abilities or average performance of both parents without the need of markers. Information on molecular markers and indicator trait(s) improved FHBs predictabilities for hybrids less related to the estimation set. Indicator traits must be observed on the predicted individuals to benefit from MTGP. Magnitudes of genetic and phenotypic correlations along with improvements in predictabilities made plant height a better indicator trait for FHBs than heading date. Thus, MTGP having only plant height as indicator trait already maximized FHBs predictabilities. Provided a good indicator trait was available, MTGP could reduce the impacts of genotype environment [Formula: see text] interaction on STGP for hybrids less related to the estimation set.